Methods of determining donor cell-free dna without donor genotype

ABSTRACT

This invention relates to methods and systems for assessing an amount of non-subject nucleic acids, such as donor-specific cell-free DNA, in a sample from a subject. The methods and systems can include the simulation of non-subject genotype when unknown. The methods and systems provided herein can be used to determine risk of a condition, such as transplant rejection.

RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of thefiling date of U.S. Provisional Application 62/547,098, filed Aug. 17,2017, the contents of which is incorporated by reference herein in itsentirety.

FIELD OF THE INVENTION

This invention relates to methods and systems for assessing an amount ofnon-subject nucleic acids, such as donor-specific cell-free DNA, in asample from a subject. The invention provides systems for analyzingand/or assessing an amount of non-subject nucleic acids in a sample froma subject without non-subject genotype information. The methods,compositions, and systems provided herein can be used to determine riskof a condition, such as transplant rejection.

SUMMARY

The present disclosure is based, at least in part, on the surprisingdiscovery of methods of determining amounts of cell-free DNA, such asnon-subject and/or subject cell-free DNA, without the need for knowledgeof the non-subject genotype. Described are these methods and systems forthe quantification of cf-DNA in subjects, such as transplant subjects,that can be used as a noninvasive assay, such as for the diagnosis ofacute rejection and/or clinically significant adverse events, withoutthe need to know the non-subject genotype (e.g., donor genotype). Themethods and systems can also be used to determine subjects at low orhigh risk, such as of rejection and/or clinically adverse events. Themethods and systems can also be used to monitor any of the subjectsprovided herein. In some embodiments, the methods and systems employ asimulation (e.g., Monte Carlo simulation) of non-subject genotype (e.g.,donor genotype). Such methods and systems can be employed in anyinstances where the sample is of mixed genotypes and the non-subjectgenotype is not known. The examples and text that refer to the scenarioof transplant subjects is for exemplification, and is not intended toimply that the assays must be so limited.

In one aspect, a method of determining an amount of non-subject nucleicacids in a sample from a subject is provided. In some embodiments, themethod comprises analyzing amounts of alleles at multiple respectivetargets in a sample, and identifying quantifiable and/or informativetargets, within the sample, performing simulations with possiblegenotypes for a non-subject; and determining amounts of alleles of eachtarget attributed to the non-subject and, optionally, the subject, basedon possible or probable non-subject genotype(s) determined from thesimulation, and, optionally, determining an amount (e.g., percent orratio) of non-subject to subject cf-DNA in the sample.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises determining the subject genotype.In one embodiment of any one of the methods or systems, the method orsystem further comprises performing amplifications to determine theamounts of alleles. In one embodiment of any one of the methods orsystems, the method or system further comprises performing sequencingassays to determine the amounts of alleles.

In one embodiment of any one of the methods or systems, the sequencingassays or amplifications are performed for at least 30, 40, 50, 60, 70,80, 90, or more targets.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises calculating quality measures ondetermined amounts (e.g., percents or ratios) in the sample. The qualitymeasure of any one of the methods or systems can be any one of thequality measures provided herein or otherwise known in the art.

In one embodiment of any one of the methods or systems provided herein,the method or system comprises simulating a likely or possiblenon-subject genotype space.

In one embodiment of any one of the methods or systems provided herein,simulations (e.g., Monte Carlo simulations) are performed to determine arange of possible or probable genotypes for the non-subject.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises adjusting measured contributionsfor respective targets based on respective possible or probablegenotypes (e.g., doubling measured contribution value responsive todetermining the non-subject probable genotype is heterozygous).

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises calculating an average, such as amean or median, amount, such as a percent or ratio.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises determining each standard curveand/or sample amplification value meets a confidence threshold. In oneembodiment of any one of the methods or systems provided herein, themethod or system further comprises determining confidence values basedon analysis of at least one of a historic amplification shape,specificity of the allele-specific PCR assay (e.g., with respect to asecond allele), signal to noise ratio for a sample, slope and r-squarevalue for standard curve sets, non-amplification values obtained oninserted controls, or contamination values obtained on the sample fromnegative controls.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises fitting data obtained from thesample to a historic amplification shape.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises determining the slope andr-square value for the standard curve sets does not exceed a thresholdvalue.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises establishing a label for thenon-subject or subject at each target identified as quantifiable and/orinformative in the sample. In one embodiment of any one of the methodsor systems provided herein, the method or system further comprisesdetermining quantifiable and/or informative targets within the sampleresponsive to classifying a respective target according to genotype. Inone embodiment of any one of the methods or systems provided herein, themethod or system further comprises classifying the respective target asquantifiable and/or informative responsive to determining the subjectand non-subject have different genotypes (e.g., the subject ishomozygous for one allele and the non-subject is not homozygous orhomozygous for the other allele). In one embodiment of any one of themethods or systems provided herein, the method or system furthercomprises adjusting measured contributions for a respective targetresponsive to determining the non-subject is heterozygous (e.g.,doubling measured contribution value responsive to determining thenon-subject is heterozygous).

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises calculating a mean or median ofinformative (e.g., identified by the genotyping component) andquality-control-passed (e.g., identified by the quality controlcomponent) allele amounts (e.g., percent or ratios) and stores the meanor median values as the amount (e.g., ratio or percentage). In oneembodiment of any one of the methods or systems provided herein, themethod or system further comprises calculating a regularized robustcoefficient of variation (“rCV”) based on a distribution of theinformative and/or quantifiable targets and associated amounts (e.g.,percents or ratios). In one embodiment of any one of the methods orsystems provided herein, the method or system further comprisescalculating a robust standard deviation (“rSD”) based on a medianabsolute divergence from a median minor species proportion. In oneembodiment of any one of the methods or systems provided herein, themethod or system further comprises converting the rSD into rCV bydivision with, for example, the non-subject cf-DNA amount (e.g., ratioor percentage). In one embodiment of any one of the methods or systemsprovided herein, the method or system further comprises adjusting rSD toavoid division by zero (e.g., by adding a quarter of one percent to thedivisor). In one embodiment of any one of the methods or systemsprovided herein, the method or system further comprises identifying asample suitable for quantification based on a threshold rCV valuedetermined on a distribution of the informative and/or quantifiabletargets and associated amounts (e.g., percents or ratios).

In one embodiment of any one of the methods or systems provided herein,the or system further comprises evaluating an average minor alleleproportion of subject homozygous and non-quantifiable and/ornon-informative targets against a contamination threshold.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises calculating a discordance qualitycheck (“dQC”) value based on the average minor allele proportion of thesubject homozygous and the non-quantifiable and/or non-informativetargets and evaluate the dQC value against a threshold. In oneembodiment of any one of the methods or systems provided herein, themethod or system further comprises identifying samples suitable forquantification based on identifying a dQC value below a threshold, e.g.,0.5%.

In one embodiment of any one of the methods or systems provided herein,the non-subject is a donor. In one embodiment of any one of the methodsor systems provided herein, the sample is from a transplant subject. Inone embodiment of any one of the methods or systems provided herein, thetransplant subject is a heart transplant subject. In one embodiment ofany one of the methods or systems provided herein, the sample is from apediatric subject. In one embodiment of any one of the methods orsystems provided herein, the sample is from a pregnant subject.

In one embodiment of any one of the methods or systems provided herein,the method or system further comprises selecting an aggregate and/or the95% confidence interval of the possible or probable simulations. In oneembodiment of any one of the methods or systems provided herein, themethod further comprises selecting simulations with below median dQC andrCV and/or determining the 95% confidence interval.

Provided herein, in another aspect, is a system for analyzing a samplefrom a subject, wherein the system comprises at least one processoroperatively connected to a memory; a first component (e.g., a qualitycontrol component), executed by the at least one processor, configuredto analyze (e.g., quantitative genotyping (“qGT”)) amounts of alleles atmultiple respective targets in a sample, and identify quantifiableand/or informative targets, within the sample; a second component (e.g.,a modelling component) configured to simulate possible genotypeinformation for a non-subject; and a third component (e.g., a genotypingcomponent), executed by the at least one processor, configured todetermine amounts of alleles of each target attributed to thenon-subject and, optionally the subject, based on possible or probablenon-subject genotype(s) determined from the simulation, and, optionally,determining an amount (e.g., percent or ratio) of non-subject to subjectamounts in the sample.

In one embodiment of any one of the systems provided herein, the systemfurther comprises a fourth component (e.g., an analytic component),executed by the at least one processor, configured to calculate qualitymeasures on determined amounts (e.g., percents or ratios) in the sample.

In one embodiment of any one of the systems provided herein, the thirdcomponent is configured to simulate a likely or possible non-subjectgenotype space. In one embodiment of any one of the systems providedherein, the third component is configured to execute a simulation (e.g.,Monte Carlo simulation) to determine a range of possible or probablegenotypes for the non-subject. In one embodiment of any one of thesystems provided herein, the third component is configured to adjustmeasured contributions for respective targets based on respectivepossible or probable genotypes (e.g., doubling measured contributionvalue responsive to determining the non-subject possible probablegenotype is heterozygous).

In one embodiment of any one of the systems provided herein, the atleast one processor is configured to calculate an average, such as amean median, amount (e.g., percent or ratio).

In one embodiment of any one of the systems provided herein, the firstcomponent is configured to determine each standard curve and/or sampleamplification value meets a confidence threshold. In one embodiment ofany one of the systems provided herein, the first component isconfigured to determine confidence values based on analysis of at leastone of a historic amplification shape, specificity of theallele-specific PCR assay (e.g., with respect to a second allele),signal to noise ratio for a sample, slope and r-square value forstandard curve sets, non-amplification values obtained on insertedcontrols, or contamination values obtained on the sample from negativecontrols. In one embodiment of any one of the systems provided, thefirst component is configured to fit data obtained from the sample to ahistoric amplification shape. In one embodiment of any one of thesystems provided, the first component is configured to determine theslope and r-square value for the standard curve sets does not exceed athreshold value.

In one embodiment of any one of the systems provided herein, the firstor third component is configured to establish a label for thenon-subject or subject at each target identified as quantifiable and/orinformative in the sample. In one embodiment of any one of the systemsprovided, the first or third component is configured to determinequantifiable and/or informative targets within the sample responsive toclassifying a respective target according to genotype. In one embodimentof any one of the systems provided, the third component is configured toclassify the respective target as quantifiable and/or informativeresponsive to determining the subject and non-subject have differentgenotypes (e.g., the subject is homozygous for one allele and thenon-subject is not homozygous or homozygous for the other allele).

In one embodiment of any one of the systems provided herein, the thirdcomponent is configured to adjust measured contributions for arespective target responsive to determining the non-subject isheterozygous (e.g., doubling measured contribution value responsive todetermining the non-subject is heterozygous). In one embodiment of anyone of the systems provided herein, the third component calculates amean or median of informative (e.g., identified by the genotypingcomponent) and quality-control-passed (e.g., identified by the qualitycontrol component) allele ratios and stores the median values as anamount (e.g., the ratio or percentage).

In one embodiment of any one of the systems provided herein, any one ofthe components (e.g., the analytic component) is configured to calculatea regularized robust coefficient of variation (“rCV”) based on adistribution of the informative and/or quantifiable targets andassociated amounts (e.g., percents or ratios). In one embodiment of anyone of the systems provided herein, any one of the components (e.g., theanalytic component) is configured to calculate a robust standarddeviation (“rSD”) based on a median absolute divergence from a medianminor species proportion. In one embodiment of any one of the systemsprovided herein, any one of the components (e.g., the analyticcomponent) is configured to convert the rSD into rCV by division with,for example, the non-subject cf-DNA amount (e.g., percentage or ratio).In one embodiment of any one of the systems provided, the component isconfigured to adjust rSD to avoid division by zero (e.g. by adding aquarter of one percent). In one embodiment of any one of the systemsprovided herein, the system is configured to identify a sample suitablefor quantification based on a threshold rCV value determined on adistribution of the informative and/or quantifiable targets andassociated amounts (e.g., percents or ratios). In one embodiment of anyone of the systems provided herein, the system is configured to evaluatean average minor allele proportion of subject homozygous andnon-informative targets against a contamination threshold.

In one embodiment of any one of the systems provided herein, the systemis configured to calculate a discordance quality check (“dQC”) valuebased on the average minor allele proportion of the subject homozygousand the non-quantifiable and/or non-informative targets and evaluate thedQC value against the threshold. In one embodiment of any one of thesystems provided, the system is configured to identify samples suitablefor quantification based on identifying a dQC value threshold, e.g.,below 0.5%.

In one embodiment of any one of the systems provided herein, the systemis further configured to select an aggregate and/or the 95% confidenceinterval of the possible or probable simulations.

In one embodiment of any one of the systems provided herein, the systemis further configured to select simulations with below median dQC andrCV and/or determining the 95% confidence interval.

In one aspect, a report comprising any one or more values that resultfrom any one of the methods or systems described herein is provided.

Provided herein, in another aspect, is a method of treating a subject.The method comprises evaluating a subject based on any one or morevalues that result from any one of the preceding methods or systems, andtreating, recommending a treatment, changing a treatment, furthermonitoring or recommending further monitoring of the subject.

In one embodiment, any one of the embodiments for the methods providedherein can be an embodiment for any one of the compositions, systems, orreports provided herein. In one embodiment, any one of the embodimentsfor the systems provided herein can be an embodiment for any one of thecompositions, methods, or reports provided herein.

BRIEF DESCRIPTION OF FIGURES

The accompanying figures are not intended to be drawn to scale. Thefigures are illustrative only and are not required for enablement of thedisclosure.

FIG. 1A shows the experimental determination of a threshold point(“cutpoint”) for CR2 with donor genotype information.

FIG. 1B shows the experimental determination of a threshold point(“cutpoint”) for CR2 without donor genotype information.

FIG. 2A shows the experimental determination of a threshold point(“cutpoint”) for graft vasculopathy with donor genotype information.

FIG. 2B shows the experimental determination of a threshold point(“cutpoint”) for graft vasculopathy without donor genotype information.

FIG. 3 is a block diagram of an example embodiment of a sample analysissystem.

FIG. 4 is a block diagram of an example distributed computer system onwhich various aspects and functions of the disclosure are practiced.

FIG. 5 is a block diagram of a sample analysis platform, according toone embodiment.

DETAILED DESCRIPTION

Accordingly, various aspects provide techniques to detect, analyze,and/or quantify nucleic acids (e.g., cell-free DNA), such as non-subjectnucleic acids (e.g., non-subject cell-free DNA), in samples obtainedfrom a subject. As used herein, “non-subject nucleic acids” refers tonucleic acids that are from another source or are mutated versions of anucleic acid found in a subject (with respect to a specific sequence,such as a wild-type sequence). “Subject nucleic acids” therefore, arenucleic acids that are not from another source and are not mutatedversions of a nucleic acid found in a subject (with respect to aspecific sequence, such as a wild-type sequence). As used herein, anyone of the methods or systems provided herein can be used to determinean amount of cell-free DNA from a non-subject source, such as DNAspecific to a donor or donor-specific cell-free DNA (e.g.,donor-specific cf-DNA) or fetal DNA (e.g., fetal cell-free DNA). Any oneof the methods or systems provided herein may be used on a sample from asubject that has undergone a transplant. In some embodiments, thetransplant is a heart transplant. Any one of the methods or systemsprovided herein may be used on a sample from a pregnant subject.

“Cell-free DNA” (cf-DNA) refers to fragments of DNA that are thereleased from cells, without wishing to be bound by any theory,generally during apoptosis, lysis, necrosis, or injury which are foundfreely circulating, e.g., in the blood, plasma, serum, urine, etc. of asubject, As used herein, the compositions and methods provided hereincan be used to determine an amount of cell-free DNA, for examplenon-subject cell-free DNA, such as of a donor that can be found in atransplant recipient or such as of a pregnant subject. “Subject” cf-DNAcan be uniquely quantified and detected as distinct from “non-subject”cf-DNA, such as in the case of transplant subjects or fetal DNA inmaternal serum during pregnancy (Norton et. al., N Engl J Med 373: 2582(2015)).

The systems and methods provided herein can employ the use ofsimulations, such as Monte Carlo simulations, when the non-subjectgenotype is not known. Generally, the systems and methods analyzeamounts of alleles at a number of targets. A “target” is a nucleic acidsequence within which there is, may be or there is an expectation ofsequence identity variability. In an embodiment, the target is, may beor is expected to be one where there is sequence variability at a singlenucleotide, such as in a population of individuals or as a result of amutation that can occur in a subject and that can be associated with adisease or condition. The target, thus, has or is expected to have morethan one allele, and in preferred embodiments, the target is biallelic.A “plurality of targets” refers to more than one target (i.e., multiplelaigels).

In some embodiments of any one of the systems or methods provided,amounts of alleles are analyzed at at least 25, 30, 35, 40, 45, 50, 55,60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95 or more targets. In someembodiments of any one of the methods or systems provided herein,amounts of alleles are analyzed at fewer than 105, 104, 103, 102,101,100, 99, 98 or 97 targets. In some embodiments of any one of themethods or systems provided herein, amounts of alleles are analyzed forbetween 40-105, 45-105, 50-105, 55-105, 60-105, 65-105, 70-105, 75-105,80-105, 85-105, 90-105, 90-104, 90-103, 90-102, 90-101, 90-100, 90-99,91-99, 92-99, 93, 99, 94-99, 95-99, or 90-95 targets. In someembodiments of any one of the methods or systems provided, for between40-99, 45-99, 50-99, 55-99, 60-99, 65-99, 70-99, 75-99, 80-99, 85-99,90-99, 90-99, 90-98, 90-97 or 90-96 targets. In still other embodimentsof any one of the methods or systems provided, for between 40-95, 45-95,50-95, 55-95, 60-95, 65-95, 70-95, 75-95, 80-95, 85-95, or 90-95targets. In still other embodiments of any one of the methods or systemsprovided, for between 40-90, 45-90, 50-90, 55-90, 60-90, 65-90, 70-90,75-90, 80-90, or 85-90 targets. In still other embodiments of any one ofthe methods or systems provided, for between 40-85, 45-85, 50-85, 55-85,60-85, 65-85, 70-85, 75-85, or 80-85 targets. In still other embodimentsof any one of the methods or systems provided, for between 40-80, 45-80,50-80, 55-80, 60-80, 65-80, 70-80, or 75-80 targets. In still otherembodiments of any one of the methods or systems provided, for between40-75, 45-75, 50-75, 55-75, 60-75, 65-75, or 70-75 targets.

Targets may be identified as quantifiable (i.e., that an allele amountcan be measured) and/or informative. “Informative targets” as providedherein are those where amounts of the alleles can be used to quantify anamount of non-subject nucleic acids relative to or distinguished fromsubject nucleic acids in a sample. Generally, informative results canexclude the results where the subject nucleic acids are heterozygous fora specific target as well as “no call” or erroneous call results. Fromthe informative results, allele amounts (e.g., ratios or percentages)can be calculated, such as using standard curves, in some embodiments ofany one of the methods or systems provided. In some embodiments of anyone of the methods or systems provided, the amount of non-subject and/orsubject nucleic acids represents an average across informative resultsfor the non-subject and/or subject nucleic acids, respectively. In someembodiments of any one of the methods or systems provided herein, thisaverage is given as an absolute amount or as a ratio or percentage.Preferably, in some embodiments of any one of the methods or systemsprovided herein, this average is a mean or the median. In otherembodiments of any one of the methods or systems provided herein, theaverage is a trimmed mean. As used herein, the “trimmed mean” refers tothe removal of the lowest reporting targets (such as the two lowest) incombination with the highest of the reporting targets (such as the twohighest). In still other embodiments of any one of the methods orsystems provided herein, the average is the mean.

In another aspect are reports of any one of more of the values producedusing any one of the methods or systems provided herein. In oneembodiment, the report provides an amount of non-subject cell-free DNAat one or more time points. In one embodiment, the report can includeand/or can also include any one or more other values produced by any oneof the methods or systems provided herein. Preferably, a report is onein which at least one of the values can be used by a clinician forassessing the subject and/or treating a subject. Any one or more of themethods provided herein can include a step of generating a report and/orproviding a report and/or assessing a subject based on one or morevalues and/or treating a subject based on one or more values produced byany one of the methods or systems or provided in any one of the reportsprovided herein.

Reports may be in oral, written (or hard copy) or electronic form, suchas in a form that can be visualized or displayed. In some embodiments,the “raw” results provided herein are provided in a report, and fromthis report, further steps can be taken to determine the amount ofnon-subject nucleic acids in the sample. In other embodiments, thereport provides the amount of non-subject nucleic acids in the sample.From the amount, in some embodiments, a clinician may assess the needfor a treatment for the subject or the need to monitor the subject, suchas the amount of the non-subject nucleic acids later in time.Accordingly, in any one of the methods provided herein, the method caninclude assessing the amount of non-subject nucleic acids in the subjectat another point in time. Such assessing can be performed with any oneof the methods provided herein. In some embodiments, the report providesamounts of non-subject nucleic acids from a subject over time.

In some embodiments of any one of the methods or systems providedherein, the amounts are in or entered into a database. In one aspect, adatabase with such values is provided. From the amount(s), a clinicianmay assess the need for a treatment or monitoring of a subject.Accordingly, in any one of the methods provided herein, the method caninclude assessing the amounts in the subject at more than one point intime. Such assessing can be performed with any one of the methods orsystems provided herein.

As used herein, “amount” refers to any quantitative value for themeasurement of nucleic acids (e.g., cf-DNA) and can be given in anabsolute or relative amount. Further, the amount can be a total amount,frequency, ratio, percentage, etc. As used herein, the term “level” canbe used instead of “amount” but is intended to refer to the same typesof values. Generally, unless otherwise provided, the amounts providedherein represent the ratio or percentage of non-subject nucleic acids ina sample.

In some embodiments, any one of the methods or systems provided hereincan comprise an analytic component configured to compare an amount to athreshold value, or to one or more prior amounts, to identify a subjectat increased or decreased risk. For example, the analytic component cansimulate a donor genotype to enable analysis of a mixed genotype samplewhere the non-subject genotype is unknown. In another example, theanalytic component is configured to compare a value obtained (reflectiveof an amount of non-subject (e.g., donor) nucleic acids (e.g., cf-DNA))in a sample against target threshold for increased risk. Where ameasurement or value falls below the thresholds the subject can belabeled low risk or in some instance not increased risk, and where thevalues exceed the threshold the subject can he identified as increasedrisk. The analytic component can also compare the measurement or valueagainst thresholds for reduced risk. If the subject is below thethresholds, the subject can be identified as low risk. If not thesubject can received no label or also be evaluated against high riskthresholds.

“Threshold” or “threshold value”, as used herein, refers to anypredetermined level or range of levels that is indicative of something.For example, in determining risk this threshold can be of the presenceor absence of a condition or the presence or absence of a risk. Thethreshold value can take a variety of forms. It can be single cut-offvalue, such as a median or mean. It can be established based uponcomparative groups, such as where the risk in one defined group isdouble the risk in another defined group. It can be a range, forexample, where the tested population is divided equally (or unequally)into groups, such as a low-risk group, a medium-risk group and ahigh-risk group, or into quadrants, the lowest quadrant being subjectswith the lowest risk and the highest quadrant being subjects with thehighest risk. The threshold value can depend upon the particularpopulation selected or the purpose of the value that is being measuredand compared to a threshold. Appropriate values, ranges and categoriesof thresholds can be selected with no more than routine experimentationby those of ordinary skill in the art.

Because of the ability to determine amounts of non-subject nucleicacids, even at low levels, the methods and systems provided herein canbe used to assess a risk in a subject, such as a transplant recipient orpregnant subject. A “risk” as provided herein, refers to the presence orabsence of any undesirable condition in a subject (such as a transplantrecipient), or an increased likelihood of the presence or absence ofsuch a condition, e.g., transplant rejection. As provided herein“increased risk” refers to the presence of any undesirable condition ina subject or an increased likelihood of the presence of such acondition. As provided herein, “decreased risk” refers to the absence ofany undesirable condition in a subject or a decreased likelihood of thepresence (or increased likelihood of the absence) of such a condition.In some embodiments of any one of the methods provided herein, a subjecthaving an increased amount compared to a threshold value, or to one ormore prior amounts, is identified as being at increased risk. In someembodiments of any one of the methods provided herein, a subject havinga decreased or similar amount compared to a threshold value, or to oneor more prior amounts, is identified as being at decreased or notincreased risk.

As an example, early detection of rejection following implantation of atransplant (e.g., a heart transplant) can facilitate treatment andimprove clinical outcomes. Transplant rejection remains a major cause ofgraft failure and late mortality and generally requires lifelongsurveillance monitoring. Treatment of transplant rejections withimmunosuppressive therapy has been shown to improve treatment outcomes,particularly if rejection is detected early. A clinician can make anassessment (e.g., assessing the risk) of a transplant subject with anamount of donor cf-DNA and such a step can be included as part of anyone of the methods provided herein.

Accordingly, in some embodiments of any one of the methods or systemsprovided, the subject is a recipient of a transplant, and the risk is arisk associated with the transplant. In some embodiments of any one ofthe methods or systems provided, the risk associated with the transplantis risk of transplant rejection, an anatomical problem with thetransplant or injury to the transplant. In some embodiments of any oneof the methods or systems provided, the injury to the transplant isinitial or ongoing injury. In some embodiments of any one of the methodsor systems provided, the risk associated with the transplant is an acutecondition or a chronic condition. In some embodiments of any one of themethods or systems provided, the acute condition is transplant rejectionincluding cellular rejection or antibody mediated rejection. In someembodiments of any one of the methods or systems provided, the chroniccondition is graft vasculopathy. In some embodiments of any one of themethods or systems provided, the risk associated with the transplant isindicative of the severity of the injury. In some embodiments of any oneof the methods or systems provided, the risk associated with thetransplant is risk or status of an infection. The risk in a recipient ofa transplant can be determined as part of any one of the methodsprovided herein.

As used herein, “transplant” refers to the moving of tissue or an organor portion thereof from a donor to a recipient for the purpose ofreplacing the recipient's damaged or absent tissue or organ or portionthereof. The transplant may be of one organ or more than one organ.Examples of organs that can be transplanted include, but are not limitedto, the heart, kidney(s), kidney, liver, lung(s), pancreas, intestine,etc. Any one of the methods or systems provided herein may be used on asample from a subject that has undergone a transplant of any one or moreof the tissues or organs, or portions thereof, provided herein. In someembodiments, the transplant is a heart transplant.

In some embodiments of any one of the methods or systems providedherein, the method or system can comprise correlating an increase in anamount of non-subject nucleic acids relative to subject or total nucleicacids with an increased risk of a condition, such as transplantrejection. In some embodiments of any one of the methods or systemsprovided herein, correlating comprises comparing an amount (e.g.,concentration, ratio or percentage) of non-subject nucleic acids to athreshold value to identify a subject at increased or decreased risk ofa condition. In some embodiments of any one of the methods or systemsprovided herein, a subject having an increased amount of non-subjectnucleic acids compared to a threshold value is identified as being atincreased risk of a condition. In some embodiments of any one of themethods or systems provided herein, a subject having a decreased orsimilar amount of non-subject nucleic acids compared to a thresholdvalue is identified as being at decreased risk of a condition.

Changes in the amounts of non-subject nucleic acids can also bemonitored over time, and any one of the methods or systems providedherein can include a step of doing so. This can allow for themeasurement of variations in a clinical state and/or permit calculationof normal values or baseline levels. In organ transplantation, this canform the basis of an individualized non-invasive screening test forrejection or a risk of a condition associated thereto. Generally, asprovided herein, the amount, such as the ratio or percent, ofnon-subject nucleic acids can be indicative of the presence or absenceof a risk associated with a condition, such as risk associated with atransplant, such as rejection, in the recipient, or can be indicative ofthe need for further testing or surveillance. In one embodiment of anyone of the methods or systems provided herein, the method or system mayfurther include an additional test(s) for assessing a condition, such astransplant rejection, transplant injury, etc., or a step of suggestingsuch further testing to the subject (or providing information about suchfurther testing). The additional test(s) may be any one of the methodsor systems provided herein. The additional test(s) may be any one of theother methods or systems provided herein or otherwise known in the artas appropriate.

Any one of the method or systems provided herein can include a step of“determining a treatment regimen”, which refers to the determination ofa course of action for the treatment of the subject. In one embodimentof any one of the methods or systems provided herein, determining atreatment regimen includes determining an appropriate therapy orinformation regarding an appropriate therapy to provide to a subject. Inany one of the methods or systems provided herein, the determining caninclude providing an appropriate therapy or information regarding anappropriate therapy to a subject. In some embodiments, the therapy isadministration of an anti-rejection treatment and/or anti-infectiontreatment. As used herein, information regarding a treatment or therapyor monitoring may be provided in written form or electronic form. Insome embodiments, the information may be provided as computer-readableinstructions. In some embodiments, the information may be providedorally.

“Administering” or “administration” or “administer” or the like meansproviding a material to a subject in a manner that is pharmacologicallyuseful directly or indirectly. Thus, the term includes directing, suchas prescribing, the subject or another party to administer the material.Administration of a treatment or therapy may be accomplished by anymethod known in the art (see, e.g., Harrison's Principle of InternalMedicine, McGraw Hill Inc.). Preferably, administration of a treatmentor therapy occurs in a therapeutically effective amount. Administrationmay be local or systemic. Administration may be parenteral (e.g.,intravenous, subcutaneous, or intradermal) or oral. Compositions fordifferent routes of administration are known in the art (see, e.g.,Remington's Pharmaceutical Sciences by E. W. Martin).

In some embodiments, the anti-rejection treatment administered is animmunosuppressive. Immunosuppressives include, but are not limited to,corticosteroids (e.g., prednisolone or hydrocortisone), glucocorticoids,cytostatics, alkylating agents (e.g., nitrogen mustards(cyclophosphamide), nitrosoureas, platinum compounds, cyclophosphamide(Cytoxan)), antimetabolites (e.g., folic acid analogues, such asmethotrexate, purine analogues, such as azathioprine and mercaptopurine,pyrimidine analogues, and protein synthesis inhibitors), cytotoxicantibiotics (e.g., dactinomycin, anthracyclines, mitomycin C, bleomycin,mithramycin), antibodies (e.g., anti-CD20, anti-IL-1, anti-IL-2Ralpha,anti-T-cell or anti-CD-3 monoclonals and polyclonals, such as Atgam, andThymoglobuline), drugs acting on immunophilins, ciclosporin, tacrolimus,sirolimus, interferons, opiods, TNF-binding proteins, mycophenolate,fingolimod and myriocin. In some embodiments, anti-rejection therapycomprises blood transfer or marrow transplant. Therapies can alsoinclude therapies for treating systemic conditions, such as sepsis. Thetherapy for sepsis can include intravenous fluids, antibiotics, surgicaldrainage, early goal directed therapy (EGDT), vasopressors, steroids,activated protein C, drotrecogin alfa (activated), oxygen andappropriate support for organ dysfunction. This may include hemodialysisin kidney failure, mechanical ventilation in pulmonary dysfunction,transfusion of blood products, and drug and fluid therapy forcirculatory failure. Ensuring adequate nutrition—preferably by enteralfeeding, but if necessary by parenteral nutrition—can also be includedparticularly during prolonged illness. Other associated therapies caninclude insulin and medication to prevent deep vein thrombosis andgastric ulcers.

In some embodiments, wherein infection is indicated, therapies fortreating a recipient of a transplant can also include therapies fortreating a bacterial, fungal and/or viral infection. Such therapiesinclude antibiotics. Other examples include, but are not limited to,amebicides, aminoglycosides, anthelmintics, antifungals, azoleantifungals, echinocandins, polyenes, diarylquinolines, hydrazidederivatives, nicotinic acid derivatives, rifamycin derivatives,streptomyces derivatives, antiviral agents, chemokine receptorantagonist, integrase strand transfer inhibitor, neuraminidaseinhibitors, NNRTIs, NS5A inhibitors, nucleoside reverse transcriptaseinhibitors (NRTIs), protease inhibitors, purine nucleosides,carbapenems, cephalosporins, glycylcyclines, leprostatics, lincomycinderivatives, macrolide derivatives, ketolides, macrolides, oxazolidinoneantibiotics, penicillins, beta-lactamase inhibitors, quinolones,sulfonamides, and tetracyclines. Other such therapies are known to thoseof ordinary skill in the art. Any one of the methods provided herein caninclude administering or suggesting an anti-infection treatment to thesubject (including providing information about the treatment to thesubject, in some embodiments). In some embodiments, an anti-infectiontreatment may be a reduction in the amount or frequency in animmunosuppressive therapy or a change in the immunosuppressive therapythat is administered to the subject. Other therapies are known to thoseof ordinary skill in the art.

Any one of the method or systems provided herein can include a step of“determining a monitoring regimen”, which refers to determining a courseof action to monitor a condition in the subject over time. In oneembodiment of any one of the methods or systems provided herein,determining a monitoring regimen includes determining an appropriatecourse of action for determining the amount of non-subject nucleic acidsin the subject over time or at a subsequent point in time, or suggestingsuch monitoring to the subject. This can allow for the measurement ofvariations in a clinical state and/or permit calculation of normalvalues or baseline levels (as well as comparisons thereto). In someembodiments of any one of the methods or systems provided hereindetermining a monitoring regimen includes determining the timing and/orfrequency of obtaining samples from the subject.

As used herein, the sample from a subject can be a biological sample.Examples of such biological samples include whole blood, plasma, serum,urine, etc. In some embodiments of any one of the methods providedherein, addition of further nucleic acids, e.g., a standard, to thesample can be performed.

In any one of the methods or systems provided herein, amounts of allelescan be determined with sequencing, such as a next generation orhigh-throughput sequencing and/or genotyping technique. Examples of nextgeneration and high-throughput sequencing and/or genotyping techniquesinclude, but are not limited to, massively parallel signaturesequencing, polony sequencing, 454 pyrosequencing, Illumina (Solexa)sequencing, SOLiD sequencing, ion semiconductor sequencing, DNA nanoballsequencing, heliscope single molecule sequencing, single molecule realtime (SMRT) sequencing, MassARRAY®, and Digital Analysis of SelectedRegions (DANSR™) (see, e.g., Stein R A (1 Sep. 2008). “Next-GenerationSequencing Update”. Genetic Engineering & Biotechnology News 28 (15);Quail, Michael; Smith, Miriam E; Coupland, Paul; Otto, Thomas D; Harris,Simon R; Connor, Thomas R; Bertoni, Anna; Swerdlow, Harold P; Gu, Yong(1 Jan. 2012). “A tale of three next generation sequencing platforms:comparison of Ion torrent, pacific biosciences and illumina MiSeqsequencers”. BMC Genomics 13 (1): 341; Liu, Lin; Li, Yinhu; Li, Siliang;Hu, Ni; He, Yimin; Pong, Ray; Lin, Danni; Lu, Lihua; Law, Maggie (1 Jan.2012). “Comparison of Next-Generation Sequencing Systems”. Journal ofBiomedicine and Biotechnology 2012: 1-11; Qualitative and quantitativegenotyping using single base primer extension coupled withmatrix-assisted laser desorption/ionization time-of-flight massspectrometry (MassARRAY®). Methods Mol Biol. 2009; 578:307-43; Chu T,Bunce K, Hogge W A, Peters D G. A novel approach toward the challenge ofaccurately quantifying fetal DNA in maternal plasma. Prenat Diagn 2010;30:1226-9; and Suzuki N, Kamataki A, Yamaki J, Homma Y. Characterizationof circulating DNA in healthy human plasma. Clinica chimica acta;International Journal of Clinical Chemistry 2008; 387:55-8). Suchmethods may also be used to determine genotype in some embodiments.

In any one of the methods or systems provided herein, amounts of allelescan be determined with an amplification technique, such a method asdescribed herein or in U.S. Publication No. WO 2016/176662. Any one ofsuch techniques are incorporated herein.

In some embodiments of any one of the methods provided herein, theamplification is performed with PCR, such as quantitative PCR meaningthat amounts of nucleic acids can be determined. Quantitative PCRinclude real-time PCR, digital PCR, TAQMAN™, etc. In some embodiments ofany one of the methods or systems provided herein the PCR is “real-timePCR”. Such PCR refers to a PCR reaction where the reaction kinetics canbe monitored in the liquid phase while the amplification process isstill proceeding. In contrast to conventional PCR, real-time PCR offersthe ability to simultaneously detect or quantify in an amplificationreaction in real time. Based on the increase of the fluorescenceintensity from a specific dye, the concentration of the target can bedetermined even before the amplification reaches its plateau. In someembodiments of any one of the methods provided, the PCR is digital PCR.

System Implementation

According to one aspect, a system is provided for calculating qualitymeasures on a sample taken from a subject, such as a transplantrecipient. Various embodiments of any one of the systems are configuredto identify samples having higher or lower risk properties responsive toanalyzing genomic data obtained from a subject. FIG. 3 illustrates oneexample system 300 for identifying such samples and risk profile.According to one embodiment of any one of the systems, the system can beconfigured to analyze the sample directly or data regarding the sampleto provide “quantitative genotyping” (qGT). According to someembodiments of any one of the systems, the system executes quantitativegenotyping that uses standard curves of heterozygous DNA sources toquantify the A and B alleles at each target. Further embodiments of anyone of the systems execute quality control procedures to evaluate eachstandard curve and sample amplification according to acceptabilitycriteria. According to some embodiments of any one of the systems, thesystem can be configured to classify data that meets the quality controlprocedures as quantifiable targets, and execute interpretationalgorithms on the quality controlled data.

According to some embodiments of any one of the systems, quality controlis based on specific acceptability criteria which can include analysisof any one or more and any combination of the following: historicamplification shape, specificity of the allele specific PCR assay withrespect to the second allele, Cp or Ct values, PCR efficiency, signal tonoise, slope and r-squared of standard curve sets, non-amplification ofcontrols, or contamination of negative controls.

According to one embodiment of any one of the systems, the systemincludes a quality control component 302 that executes the analysisand/or disclosed algorithms for identifying quantifiable targets.

According to some embodiments of any one of the systems, the system(e.g., 300) provides a primary analysis of genotype. For example, thesystem can first evaluate recipient (or subject) and donor (ornon-subject) genomes for “basic genotyping” (bGT). The bGT processgenerates labels for the donor (or non-subject) and/or recipient (orsubject) three possible genotypes at each target (e.g., homozygous AA,heterozygous AB, and homozygous BB). According to various embodiments ofany one of the systems, this information is used by the system in theinterpretation of the qGT per target. According to one embodiment of anyone of the systems, the system 300 can include a genotyping component304 configured to analyze genotype of a donor (or non-subject) and/orrecipient (or subject) contribution to a sample at specified targets.According to one embodiment of any one of the systems, theidentification of the genotype at each target allows the system torecognize informative targets, such as fully and/or half informative,based on genotype.

For example, the system can be configured to define informative targetsas those where the recipient (or subject) is known homozygous and thedonor (or non-subject) has another genotype. In one example, the systemidentifies the informative targets, stores information on respectivetargets that are informative and includes the labels for the donorand/or recipient and the result of analyzing genotype of both.

According to another embodiment of any one of the systems, a genotypingcomponent (e.g., 304) labels donor (or non-subject) and/or recipient (orsubject) targets to analyze the informative targets. In another example,the system is configured to identify an informative target, where thedonor (or non-subject) is homozygous for the other allele (differentfrom a homozygous recipient (or subject)). In further embodiments of anyone of the systems, the genotyping component can be configured toclassify respective targets as fully informative or half informativeresponsive to analysis of observed allele ratios.

In this example, the target is referred to as fully informative, and theobserved allele ratio is approximately the overall donor cf-DNA (ornon-subject cf-DNA) level. In further examples, instances are identifiedby the system where the donor (or non-subject) is heterozygous and therecipient (or subject) is homozygous, and the target is defined as halfinformative (because the contribution is to both the A and B alleles).For half informative targets, the system is configured to adjust themeasured contribution. For example, responsive to determining a targetis half informative the measured contribution can be doubled. In otherembodiments, more refined adjustments can be executed. For example,ratios of donor cf-DNA to recipient cf-DNA can be expressed aspercentages. The percent value can be used to adjust measuredcontributions accordingly. In other example, the adjustment to themeasured contribution can be based on statistical variation, among otheroptions.

According to various embodiments of any one of the systems, the systemis configured to generate the median of informative andquality-control-passed allele ratios and output the median as thepercentage of donor cell free DNA (or non-subject cf-DNA). The systemcan be configured to report the median of informative andquality-control-passed allele ratios and output the median as thepercentage of donor cell free DNA to improve the robustness of thecalculated results. In some implementations of any one of the systems,the system includes a genotyping component (e.g., 304) configured tolabel donor (or non-subject) and/or recipient (or subject) targets, andadjust any measured contributions as needed.

According to one embodiment of any one of the systems, the systemexecuted qGT process generates at least two quality measures (e.g.,assessment of usefulness of a value), a robust Coefficient of Variation(rCV) and a dQC. For example, the system can be configured to calculatethe regularized (rCV) using the distribution of the informative andquantifiable targets.

In one approach, a robust standard deviation (rSD) is computed as themedian absolute divergence from the median minor species proportion,scaled by a normalizing factor (e.g., of 1.4826). The rSD can beconverted to a coefficient of variation by dividing by the donor cf-DNA% (or non-subject cf-DNA %) after it has been regularized by adding astub value (e.g., a quarter of one percent). The stub value can beintroduced by the system to avoid instability around a zero divisor, andincludes in various examples, a small value to ensure a non-zerodivisor. In various embodiments, the system can be configured to measurethe spread of assayed targets around their median with the rCV. Thisallows the system to determine the rCV as a metric of precision orsample quality. The system can be configured to apply the sample qualitymetric to identify health samples. In some examples, useful samples canhave a rCV below 50%. The result of the improved quality metrics yieldsincreases in sample anomalies detection, as well as improvement inadverse condition detection over conventional approaches.

According to one embodiment of any one of the systems, the system 300can include an analytic component 306 configured to calculate variousquality measures on sample data (including for example adjusted sampledata based on genotype). In one example, the analytic component isconfigured to calculate rSD, rCV, and dQC to ensure sample stability andensure no contamination of the sample has occurred.

According to some embodiments of any one of the systems, the systemdetermines the dQC value to provide a discordance quality check: thesystem is configured to evaluate the average minor allele proportion ofrecipient homozygous and non-informative targets as a safeguard againstsample mix-ups and contamination. “dQC” values should theoretically readnearly zero percent, subject to non-specificity allelic noise. If asample-swap had occurred during collection or processing, the wrongrecipient genotypes are used, and the dQC test executed by the systemimmediately flags up to 50 or 100% readings at presumed non-informativetargets. Further embodiments of any one of the systems, implement dQCanalysis to identify sample contamination and genomic instability in thesample. The system can be set with a default value to identify data asuseful samples when a calculated dQC value falls below, for example,0.5%. Other thresholds can be implemented (e.g., <1%, 2%, 0.3%, 0.4%,0.6%, etc.). Further example thresholds include 1%, 5%, 10%, or 50%. Invarious embodiments of any one of the systems, execution of dQCfiltering improves detection of contamination and/or detection ofgenomic instability over conventional approaches.

In a further aspect (or in further embodiments of any one of the othersystems provided), a system configured with methods to simulate donor(or non-subject) genotype and then, in some embodiments, calculate donorcf-DNA(or non-subject cf-DNA), is provided (or can be so configured).For example, if a donor (or non-subject) genotype is not available thesystem can still calculate donor cf-DNA (or non-subject cf-DNA) based onsimulation of donor (or non-subject) genotype data. Simulating donor (ornon-subject) genotype enables the system (e.g., 300) to determineprobable donor (or non-subject) genotype and ranges for probable qGToutcomes. According to various embodiments of any one of the systems,the system is configured to generate wholly random genotypes and executestatistical calculations to identify the more likely non-self genotypes.The system can repeat the random genotype generation with biases appliedto alleles which are evidently visible.

According to various embodiments of any one of the systems, the system(e.g., 300) is configured to execute a simulation method to computedonor cf-DNA (or non-subject cf-DNA) when the donor genotype is notavailable. Using just the recipient's genotypes and qGT results, thesystem evaluates donor (or non-subject) options using a Monte Carlosimulation. For example, the preliminary random selections in thesimulations determine what overall results a given qGT sample couldrepresent. The statistical analyses of the simulation findings by thesystem establish probable donor (or non-subject) genotypes. The systemcan also be configured to execute secondary Monte Carlo simulations toexplore the likely donor (or non-subject) genotype space and yield arange of probable qGT outcomes. According to one example, each of fiftythousand simulations executed by the system reports a median donorcf-DNA (or non-subject cf-DNA), rCV and dQC triplet, creating a threedimensional point cloud. In subsequent processing on the system, thepoint cloud is sliced for the lower-third of dQC and rCV and theremaining “quadrant” represents the simulations corresponding to arealistic and clean sample. The central 95% of the donor cf-DNA (ornon-subject cf-DNA) calls can yield a “Method 2” outcome for the qGTwithout having donor (or non-subject) genotype, in some embodiments. Inother implementations, fewer simulations (e.g., ten thousand, twentythousand, thirty thousand, etc.) can be executed or a larger number ofsimulations (e.g., sixty thousand, seventy thousand, etc.) can beexecuted to establish values for processing. According to someembodiments of any one of the systems, additional calculations can beapplied to refine genotype simulations and resulting predictions ofdonor genotype.

Various aspects and functions described herein (e.g., execution of basicgenotyping algorithms, specific genotyping algorithms, qGT algorithms,manipulation of sample recorded data to transform the sample results(e.g., into genotypic normalized appearance values), “without donor (ornon-subject)” algorithms, (re)simulation algorithms, Monte-Carlosimulations, etc.), may be implemented as specialized hardware orsoftware components executing in one or more specially configuredcomputer systems (e.g., network appliances, personal computers,workstations, mainframes, networked clients, servers, media servers,application servers, database servers, web servers, mobile computingdevices (e.g., smart phones, tablet computers, and personal digitalassistants) and network equipment (e.g., load balancers, routers, andswitches)). Further, aspects may be located on a single computer systemor may be distributed among a plurality of computer systems connected toone or more communications networks.

For example, various aspects, functions, system components, andprocesses (e.g., quality control component, genotyping component, andanalytic component) may be located on singular computer systems ordistributed among one or more computer systems (including cloudresources) specially configured to provide a service to one or moreclient computers, or to specially configured to perform an overall taskas part of a distributed system, such as the distributed computer system400 shown in FIG. 4. Consequently, embodiments are not limited toexecuting on any particular system or group of systems. Further,aspects, functions, and processes may be implemented in software,hardware or firmware, or any combination thereof. According to someembodiments of any one of the systems, computer system 400 can beconnected to other systems for processing tissue and/or blood samples toyield cf-DNA values or to analyze the values capture from the same todetermine sample quality, c.nntaminatinn, health and/or viahility, amongother options.

Referring to FIG. 4, there is illustrated a block diagram of a specialpurpose distributed computer system 400, in which various aspects andfunctions of the disclosure are practiced. As shown, the distributedcomputer system 400 includes one or more computer systems that exchangeinformation. More specifically, the distributed computer system 400includes computer systems 402, 404, and 406. As shown, the computersystems 402, 404, and 406 are interconnected by, and may exchange datathrough, a communication network 408. The network 408 may include anycommunication network through which computer systems may exchange data.To exchange data using the network 408, the computer systems 402, 404,and 406 and the network 408 may use various methods, protocols andstandards, including, among others, Fiber Channel, Token Ring, Ethernet,Wireless Ethernet, Bluetooth, IP, IPV6, TCP/IP, UDP, DTN, HTTP, FTP,SNMP, SMS, MMS, SS4, JSON, SOAP, CORBA, REST, and Web Services. Toensure data transfer is secure, the computer systems 402, 404, and 406may transmit data via the network 408 using a variety of securitymeasures including, for example, SSL or VPN technologies. While thedistributed computer system 400 illustrates three networked computersystems, the distributed computer system 400 is not so limited and mayinclude any number of computer systems and computing devices, networkedusing any medium and communication protocol.

As illustrated in FIG. 4, the computer system 402 includes a processor410, a memory 412, an interconnection element 414, an interface 416 anddata storage element 418. To implement at least some of the aspects,functions, and processes disclosed herein, the processor 410 performs aseries of instructions that result in manipulated data. The processor410 may be any type of processor, multiprocessor or controller. Exampleprocessors may include a commercially available processor. The processor410 is connected to other system components, including one or morememory devices 412, by the interconnection element 414.

The memory 412 stores programs (e.g., sequences of instructions coded tobe executable by the processor 410) and data during operation of thecomputer system 402. Thus, the memory 412 may be a relatively highperformance, volatile, random access memory such as a dynamic randomaccess memory (“DRAM”) or static memory (“SRAM”). However, the memory412 may include any device for storing data, such as a disk drive orother nonvolatile storage device. Various examples may organize thememory 412 into particularized and, in some cases, unique structures toperform the functions disclosed herein. These data structures may besized and organized to store values for particular data and types ofdata.

Components of the computer system 402 are coupled by an interconnectionelement such as the interconnection element 414. The interconnectionelement 414 may include any communication coupling between systemcomponents such as one or more physical busses in conformance withspecialized or standard computing bus technologies. The interconnectionelement 414 enables communications, including instructions and data, tobe exchanged between system components of the computer system 402.

The computer system 402 also includes one or more interface devices 416such as input devices, output devices and combination input/outputdevices. Interface devices may receive input or provide output. Moreparticularly, output devices may render information for externalpresentation. Input devices may accept information from externalsources. Examples of interface devices include keyboards, mouse devices,trackballs, microphones, touch screens, printing devices, displayscreens, speakers, network interface cards, etc. Interface devices allowthe computer system 402 to exchange information and to communicate withexternal entities, such as users and other systems.

The data storage element 418 includes a computer readable and writeablenonvolatile, or non-transitory, data storage medium in whichinstructions are stored that define a program or other object that isexecuted by the processor 410. The data storage element 418 also mayinclude information that is recorded, on or in, the medium, and that isprocessed by the processor 410 during execution of the program. Theinstructions may be persistently stored as encoded signals, and theinstructions may cause the processor 410 to perform any of the functionsdescribed herein. The medium may, for example, be optical disk, magneticdisk or flash memory, among others. In operation, the processor 410 orsome other controller causes data to be read from the nonvolatilerecording medium into another memory, such as the memory 412, thatallows for faster access to the information by the processor 410 thandoes the storage medium included in the data storage element 418. Thememory may be located in the data storage element 418 or in the memory412, however, the processor 410 manipulates the data within the memory,and then copies the data to the storage medium associated with the datastorage element 418 after processing is completed. A variety ofcomponents may manage data movement between the storage medium and othermemory elements and examples are not limited to particular datamanagement components. Further, examples are not limited to a particularmemory system or data storage system.

Although the computer system 402 is shown by way of example as one typeof computer system upon which various aspects and functions may bepracticed, aspects and functions are not limited to being implemented onthe computer system 402 as shown in FIG. 4. Various aspects andfunctions may be practiced on one or more computers having a differentarchitectures or components than that shown in FIG. 4.

The computer system 402 may be a computer system including an operatingsystem that manages at least a portion of the hardware elements includedin the computer system 402. The processor 410 and operating system cantogether define a computer platform for which application programs inhigh-level programming languages are written. Additionally, variousaspects and functions may be implemented in a non-programmedenvironment. For example, documents created in HTML, XML or otherformats, when viewed in a window of a browser program, can renderaspects of a graphical-user interface or perform other functions.Further, various examples may be implemented as programmed ornon-programmed elements, or any combination thereof.

EXAMPLE

A total of 298 samples from 87 unique transplant recipient subjects bothadult and pediatric passed quality control (QC) standards and wereavailable for analysis. One individual participated in the study bothafter initial transplantation and after retransplantation and wasanalyzed as two unique subjects, given the two unique donor/recipientmismatched DNA. The mean patient age at transplant was 7.9+/−7.5 years(range 0.03 to 24.2 years); the mean age at blood sample was 12.7+/−8.1years (range 0.08 to 30.2 years); 59.6% (51/87) of the subjects weremale, and 65.5% (57/87) were white. The mean time from transplant toblood sample was 4.8+/−4.2 years.

Correlation Between Donor Fraction and Cellular Rejection Grade inBiopsy-Associated Blood Samples

A total of 158 samples were taken within 24 hours prior to EMB andincluded for analysis. Only one sample was associated with each biopsy.Results are summarized in Table 1. 134 biopsies were grade CR0, 21biopsies were grade CR1, and 3 biopsies were grade CR2.

When the donor genotype was known for the analysis, the mean donorcf-DNA fraction was found to be 0.11% (IQR 0.06-0.21%) in samplesassociated with grade CR0 biopsies, 0.37% (IQR 0.15-0.72%) in samplesassociated with grade CR1 biopsies, and 0.97% (IQR 0.88-1.06%) insamples associated with grade CR2 biopsies (p=0.027). The empiricaloptimal cutpoint for ruling out grade CR2 rejection based on theassociated ROC curve was 0.87% [95% CI 0.78-0.97% (p=0.009)]. The PPVwas 13.4% (7.6, 22.6) and the NPV was 100%. A graphical representationof the data is presented in FIG. 1A.

When the donor genotype was unknown, the mean donor cf-DNA fraction was0.25% (IQR 0.17-0.39%) in samples associated with grade CR0 biopsies,0.89% (IQR 0.44-5.35%) in samples associated with grade CR1 biopsies,and 1.22% (IQR 1.04-5.18%) in samples associated with grade CR2 biopsies(p<0.001). The empirical optimal cutpoint for ruling out grade CR2rejection based on the associated ROC curve was 0.89% [95% CI 0.46-1.70%(p=0.725)]. The PPV was 15% (3.21-37.9) and NPV was 100% (97.4, 100). Agraphical representation of the data is presented in FIG. 1B.

TABLE 1 Donor Fraction and Cellular Rejection Grade Rejection Grade CR0CR1 CR2 Null Median [IQR] Median [IQR] Median [IQR] Hypothesis*Statistical Test N 134 21 3 With Donor 0.11 [0.06, 0.21] 0.37 [0.15,0.72] 0.97 [0.88, 1.06] p = 0.027 Independent samples Genotype mediantest Without Donor Genotype Average 0.25 [0.17, 0.39] 0.89 [0.44, 5.35]1.22 [1.04, 5.18] p < 0.001 Independent samples median test *Nullhypothesis: the medians are the same across rejection grade categories(CR0 vs. CR1 vs. CR2)Correlation with Quilty Lesions

139 samples were associated with biopsies reported for presence orabsence of Quilty lesions (121 no, 18 yes). Correlations between donorcf-DNA fraction are summarized in Table 2.

When the donor genotype was known for the analysis, the mean donorcf-DNA fraction was 0.12% (IQR 0.07-0.32%) in samples associated withbiopsies negative for Quilty lesions and 0.10% (IQR 0.06-0.19%) insamples associated with biopsies positive for Quilty lesions (p=0.738).

When the donor genotype was unknown, the mean donor cf-DNA fraction was0.28% (IQR 0.18-0.53%) in samples associated with biopsies negative forQuilty lesions and was 0.21% (IQR 0.15-0.27%) in samples associated withbiopsies positive for Quilty lesions (p=0.03).

TABLE 2 Donor Fraction and Presence of Quilty Lesions Quilty Lesions NoYes Null Median [IQR] Median [IQR] Hypothesis* Statistical Test N 121 18With Donor 0.12 [0.07, 0.32] 0.10 [0.06, 0.19] p = 0.738 Independentsamples Genotype median test Without Donor Genotype Average 0.28 [0.18,0.53] 0.21 [0.15, 0.27] p = 0.03 Independent samples median test *Nullhypothesis: the medians are the same across presence/absence of guiltylesions (no vs. yes)Correlation with Coronary Artery Graft Vasculopathy (CAV)

116 blood samples were collected within 24 hours prior to selectivecoronary angiography. Of these, 11 demonstrated graft vasculopathy asdefined by the 2010 ISHLT grading system (Mehra et al., J Heart LungTransplant 29, 717-727 (2010)), and 99 showed no graft vasculopathy. Acomparison of donor cf-DNA fractions among angiography-associatedsamples is summarized in Table 3.

When the donor genotype was known for the analysis, the mean donorfraction was 0.09% (IQR 0.06-0.20%) for samples not associated with CAVand 0.47% (IQR 0.27-0.71%) for samples associated with CAV (p=0.05).Mehra, M. R., et al. International Society for Heart and LungTransplantation working formulation of a standardized nomenclature forcardiac allograft vasculopathy-2010. J Heart Lung Transplant 29, 717-727(2010). The empirical optimal cutpoint for ruling out CAV was 0.19% [95%CI 0.09-0.38% (p<0.001)]. A graphical representation of the data ispresented in FIG. 2A.

When the donor genotype was unknown for the analysis, the mean donorfraction was 0.27% (IQR 0.16-0.52%) for samples not associated with CAVand 0.55% (IQR 0.38-1.22%) for samples associated with CAV (p=0.057).The empirical optimal cutpoint for ruling out CAV was 0.37% [95% CI0.24-0.57% (p<0.001)]. A graphical representation of the data ispresented in FIG. 2B.

TABLE 3 Donor Fraction and Coronary Artery Graft Vasculopathy GraftVasculopathy No Biopsy or No CAD GV Angio Null Median [IQR] Median [IQR]Median [IQR] Hypothesis* Statistical Test N 99 11 155 With Donor 0.09[0.06, 0.20] 0.52 [0.33, 0.88] 0.32 [0.14, 0.87] p = 0.028 Independentsamples Genotype median test Without Donor Genotype Average 0.27 [0.16,0.54] 0.55 [0.38, 1.22] 0.057 p = 0.057 Independent samples median test*Null hypothesis: the medians are the same across no CAD and GV (no CADvs. GV)Correlation with Antibody-Mediated Rejection (AMR)

142 samples were associated with biopsies analyzed for antibody-mediatedrejection (AMR). 132 samples were read as pAMR0 and 3 were read as gradepAMR 1 or 2. A comparison of donor cf-DNA fractions among AMR samples issummarized in Table 4.

When the donor genotype was known for the analysis, the mean donorfraction was 0.12% (IQR 0.07-0.29%) for samples associated with gradepAMR0 and was 0.26% (IQR 0.09-0.33%) for samples associated with gradepAMR1 or 2 (p=0.905).

When the donor genotype was unknown for the analysis, the mean donorfraction was 0.29% (IQR 0.18-0.61%) for samples associated with gradepAMR0 and was 0.39 (IQR 0.12-0.44%) for samples associated with gradepAMR1 or 2 (p=0.969). The empirical optimal cutpoint for ruling outpAMR1 or 2 based on the associated ROC curve was 0.38% [95% CI0.19-0.74% (p=0.005)].

TABLE 4 Donor Fraction and Antibody-mediated Rejection Antibody MediatedRejection Grade 0 1 or 2 Null Median [IQR] Median [IQR] Hypothesis*Statistical Test N 132 3 With Donor 0.12 [0.07, 0.29] 0.26 [0.09, 0.33]p = 0.905 Independent samples Genotype median test Without DonorGenotype Average 0.29 [0.18, 0.61] 0.39 [0.12, 0.44] p = 0.969Independent samples median test *Null hypothesis: the medians are thesame across treatment for infection (0 vs. 1 or 2)

Discussion

It has been found that a targeted, high-throughput assay for thequantification of donor cf-DNA has exquisite sensitivity, such as forrejection surveillance in heart transplant recipients, and that markedelevations in the donor fraction correlate to significant allograftinjury, including acute episodic rejection and chronic rejection in theform of coronary artery graft vasculopathy. Specifically, the empiricaloptimal cutpoint of 0.87% (95% CI 0.78-0.97%) reliably distinguished CR0and CR1 from CR2 grade rejection. The donor fraction of total cf-DNA didnot distinguish between Quilty lesions, however.

Donor cf-DNA is uniquely suited as a biomarker in the field oftransplantation given the genetic differences between donor andrecipient. The field has progressed significantly since the first reportin 1998, where the presence of a Y chromosome in the serum of femalerecipients was detected (Lo et al., Lancet 351: 1329-1330 (1998)).

The use of donor cf-DNA holds promise in dramatically reducing the needfor surveillance biopsy and as such, allows for more frequent monitoringfor rejection. Both the apparent sensitivity of the assay in detectingearly rejection and the fact that it can be used at a higher frequencythan EMB or other biopsies, would allow clinicians frequent non-invasivemonitoring, which may result in both decreased trauma to the patient andearlier and more effective detection of rejection and/or otherclinically significant events. In addition, donor cf-DNA may add to theunderstanding of histopathologic patterns of heart transplantrecipients. The finding that patients with and without Quilty lesionshad similar levels of donor cf-DNA adds to the evidence that thispathologic finding may not reflect injury to the donor organ, as othershave suggested (Gopal et al., Pathol Int 48: 191-198 (1998)).Strikingly, the data showed a stepwise statistically significantdifference in donor cf-DNA levels when comparing cellular grades CR0 toCR1 to CR2. This result was unexpected, and suggests a measureablelinear relationship between levels of donor of DNA and progressiveinjury to the donor organ.

Materials and Methods Measurements and Definitions

Each subject's height and weight at time of transplant and length ofstay were recorded. Treatment of rejection was defined as change inimmunosuppressive medications with the intent to treat allograftrejection as documented in the medical record, and initiation oftreatment for rejection was recorded as the date and time thismedication change was first administered to the subject. Biopsy provencellular rejection was defined as ISHLT grade 2 or higher cellularrejection. Biopsy proven antibody-mediated rejection was defined asISHLT grade 1 or higher AMR. Mechanical circulatory support was definedas either temporary or durable ventricular assist device, aortic balloonpump, or extra-corporeal circulatory support. If a subject was diagnosedwith cancer or post-transplant lymphoproliferative disease, or becamepregnant, the first dates of diagnosis were recorded, as theseconditions introduce a confounding source of additional “non-self”cell-free DNA into the recipient serum. The pathology reports of allbiopsies were reviewed and 2004 ISHLT grade was recorded, as well as ifthe biopsy was judged to have Quilty lesions. The results of coronaryangiography, if performed within 24 hours prior to blood sample, wererecorded according to the 2010 ISHLT grading system (Mehra et al., JHeart Lung Transplant 29: 717-727 (1998)).

Blood samples were obtained from heart transplant recipients in thefollowing clinical scenarios: days 1, 4, 7, and 28 following transplant,within 24 hours prior to any EMB, and immediately prior to and then days1, 4, 7, and 28 after initiation of treatment for rejection.

Mean total cf-DNA levels and interquartile ranges (IQR) were reported inng/dL and mean percentage donor cf-DNA levels and IQRs were reported asa fraction of the total. The independent sample means test was used tocompare donor fraction (percentage donor cf-DNA) and total cf-DNA (ng/mlplasma) across the clinical variables tested.

Exclusion Criteria

In determining sensitivity and specificity of the biomarker for thepre-treatment detection of rejection, samples were excluded fromanalysis if the sample was collected within 8 days of cardiactransplantation, if the sample was taken within 28 days after theinitiation of treatment of rejection, if the sample was taken while thepatient was on mechanical circulatory support, if the subject had adiagnosis of cancer or post-transplant lymphoproliferative disease atthe time of draw, or if the sample was taken after intracardiac accessduring the biopsy procedure, as these clinical scenarios offerbiological reasons for alterations in total cf-DNA and donor fractionthat confound interpretation of assay results as they relate to theearly, pre-treatment, detection of rejection. Sensitivity andspecificity for the diagnosis of allograft rejection was based onbiopsy-associated samples that fell outside of these exclusion criteria.Subjects who were recipients of bone marrow or non-cardiac solid organtransplantation or who were pregnant prior to cardiac transplantationwere also excluded from this study given that the multipledonor/recipient (and fetal) genotypes confound analysis.

Additionally, technical exclusion of samples occurred if they did notmeet the following quality control (QC) standards for the assay: bloodvolume, plasma volume, DNA quantity, time to spin, and temperature.

Blood Sample Collection

Three to ten milliliters (ml) of anti-coagulated blood were collected toassess circulating levels of cf-DNA. Each sample was collected in 10 mlBCT tubes (Streck, Omaha, Nebr.). Samples were immediately coded,de-identified, and delivered to the laboratory for processing.

Plasma Processing and DNA Extraction

Separation of plasma from whole blood by centrifugation was carried outas previously described. Plasma was stored at −80° C. until DNAextraction. All cf-DNA extractions were performed using ReliaPrep™ HTCirculating Nucleic Acid Kit, Custom (Promega, Madison, Wis.). Totalcf-DNA from each plasma sample was also recorded. Recipient genomic DNAwas extracted by using ReliaPrep™ Large Volume gDNA Isolation system(Promega, Madison, Wis.) or Gentra Puregene Blood Kit (Qiagen,Germantown Md.). Genomic donor DNA for genotyping was obtained from theBlood Center of Southeast Wisconsin which collects and stores DNA fromall donors as part of the donor/recipient matching process. In somecases, genomic DNA was obtained from biopsy samples, and extracted usinga QIAamp DNA Micro Kit (Qiagen, Germantown Md.). All purified genomicDNA was re-suspended in 0.1× TE buffer.

Total cf-DNA Analysis

Total cf-DNA content in each plasma sample was evaluated in triplicateusing a TaqMan quantitative real-time polymerase chain reaction(qRT-PCR) reference assay that detects the Ribonuclease P RNA componentH1 (H1RNA) gene (RPPH1) on human chromosome 14, cytoband 14q11.2. Theassay amplifies an 87 bp product that maps within the single exon RPPH1gene, at chr14:20811565 on NCBI build 37 (Thermo Fisher Scientific,Waltham, Mass.). PCR analysis was carried out on an Applied BiosystemsQuantStudio 7 Flex Real-Time PCR System (Thermo Fisher Scientific,Waltham, Mass.). For each reaction, one μl of cf-DNA extracted fromplasma was used. A dilution series of human genomic DNA was used tocreate a standard curve for quantification. Total cf-DNA from eachsample was obtained and presented as ng/ml of plasma.

Percentage Donor cf-DNA Analysis

A proprietary, multiplexed, allele-specific quantitative PCR-based assaycalled the myTAI-Heart assay was designed to directly quantify thepercentage of donor cell-free DNA (Dcf-DNA) as a fraction of the totalcf-DNA (TAI Diagnostics). The assay quantifies bi-allelic SNPs withreal-time PCR specific to each allele. High frequency population SNPs instable genomic regions were selected, as this increased their likelihoodof reliable quantification and the discrimination ability betweenrecipient and donor genomes.

Fifteen ng of cf-DNA was added to a multiplexed library master mixturewith an exogenous standard (TAI5) spiked into each sample (4.5E+03copies) and amplified by PCR for 35 cycles in a 25 ul reactioncontaining 0.005 U Q5 (NEB) DNA polymerase, 0.2 mM dNTPs, 3 uM forwardprimer pool of 96 targets, and 3 uM reverse primer pool of 96 targets,at a final concentration of 2 mM MgCl₂. Cycling conditions were 98° C.for 30 s, then 35 cycles of 98° C. for 10 s, 55° C. for 40 s, and 72° C.for 30 s. This was then followed by a 2 min incubation at 72° C. Sampleswere then stored at 4° C. Ten microliters of the final reaction wascleaned up using ExoSAP-IT(Thermo Fisher Scientific) by incubating at37° C. for 15 minutes and the 80° C. for 15 minutes.

Samples were then diluted 1:1 with TAI preservation buffer and stored at−80° C. until ready for quantitative genotyping. Samples were thendiluted 1:100 for quantitative genotyping and set up as a 3 ul reactionwith appropriate controls and calibrators for a real time PCR run usinga Roche LightCycler 480 system (Roche Diagnostics, Indianapolis, Ind.).

Analysis Quantitative Genotyping

The “quantitative genotyping” (qGT) uses standard curves of heterozygousDNA sources to quantify the A and B alleles at each target. Qualitycontrol procedures evaluate each standard curve and sample amplificationto meet acceptability criteria. Quantifiable targets are theninterpreted. Acceptability criteria include historic amplificationshape, specificity of the allele-specific PCR assay with respect to thesecond allele, signal-to-noise ratio, slope and r-squared of standardcurve sets, non-amplification of controls, and contamination of negativecontrols.

The primary analysis first evaluates recipient and donor genomes for“basic genotyping” (bGT). The bGT process labels the donor and/orrecipient with three possible genotypes at each target (e.g. homozygousAA, heterozygous AB, and homozygous BB). This information is needed inorder to accurately interpret the qGT per target. Informative targetsare defined as those where the recipient is known homozygous and thedonor has another genotype. Where the donor is homozygous and differentfrom the recipient, the target is referred to as fully-informative,because the observed B allele ratio is approximately the overall donorcf-DNA level. Where the donor is heterozygous, the target is calledhalf-informative because the contribution is to both the A and Balleles, meaning the measured contribution must be doubled. Forrobustness, the median of informative and quality-control-passed alleleratios is reported as the percentage of donor cf-DNA.

Each qGT process yields two major quality measures, the rCV and dQC. Theregularized robust coefficient of variation (rCV) is computed using thedistribution of the informative and quantifiable targets. First, therobust standard deviation (rSD) is computed as the median absolutedivergence from the median minor species proportion, scaled by anormalizing factor of 1.4826. The rSD is converted to a coefficient ofvariation by dividing by the donor cf-DNA % after it has beenregularized by adding a quarter of one percent, to avoid instabilityaround a zero divisor. The rCV measures the spread of assayed targetsaround their median and serves as a metric of precision or samplequality. Useful samples will generally have an rCV below 50%.

The dQC is a discordance quality check: the average minor alleleproportion of recipient homozygous and non-informative targets isevaluated in order to safeguard against sample mixups and contamination.These should theoretically read nearly zero percent, subject tonon-specificity allelic noise. If a sample-swap had occurred duringcollection or processing, the wrong recipient genotypes are used, andthe dQC immediately flags up to 50 or 100% readings at presumednon-informative targets. The dQC also captures sample contamination andpossibly genomic instability. Useful samples will generally have a dQCbelow 0.5%.

A secondary method to compute donor cf-DNA is applicable when the donorgenotype is not available. Using just the recipient's genotypes and qGTresults, donor options are evaluated in a Monte Carlo simulation.Preliminary random selections illustrate what overall results a givenqGT sample could represent. Statistical analyses of the simulationfindings provide support for probable donor genotypes. Secondary MonteCarlo simulations explore the possible or likely donor genotype spaceand yield a range of probable qGT outcomes. Each of 50,000 simulationsreports a median Dcf-DNA, rCV and dQC triplet, and constitutes a threedimensional point cloud. The point cloud is sliced for the lower-thirdof dQC and rCV and the remaining “quadrant” represents the simulationscorresponding to a realistic and clean sample. The central 95% of theresulting donor cf-DNA calls becomes the outcome for the qGT without thedonor genotype.

Donor Fraction

Donor fractions (or percent donor cf-DNA) were calculated and comparedagainst events such as cellular rejection, antibody mediated rejection,graft vasculopathy, and clinically significant events of death, cardiacarrest, cardiac retransplantation, and the initiation of mechanicalcirculatory support. If a subject was diagnosed with cancer orpost-transplant lymphoproliferative disease, or became pregnant, thefirst dates of diagnosis were recorded, if applicable.

Genotyping of samples from subjects passed inclusion/exclusion criteriaand were used for subsequent analysis. Genotyping of each donorrecipient pair resulted in informative loci per sample.

Statistics

The median test for independent medians was performed to test whetherrejection type (CR0, CR1, CR2) has equal medians by method type (withdonor genotype or with simulations without donor genotype). Whencombining CR0 and CR1 and comparing the median of these methods to CR2,the p-values were greater than 0.05. It was, therefore, concluded thatthe medians are equal across rejection types. However, when comparingmedians across the three rejection types (CR0 vs. CR1 vs. CR2), thep-values were less than 0.05, and it was concluded that the mediansdetermined when the donor genotype was known and when the donor genotypewas unknown are not equal with respect to rejection type.

Receiver-operating characteristic (ROC) curves were constructed toassess the sensitivity and specificity of the two analytical methods andto compare their ability to diagnose CR0 vs. CR1 vs. CR2. The optimalcutoff point or decision threshold is the point that gives maximumcorrect classification and the method by Liu et al. (Stat Med.31(23):2676-86 (2012)) was used. This method maximizes the product ofthe sensitivity and specificity. The negative and positive predictivevalues of the tests were also computed. For example, a positivepredictive value (PPV) of 13.4% represents that, among those who had apositive screening test, the probability of disease was 13.4%. Likewise,a negative predictive value (NPP) of 100% shows that, among those whohad a negative screening test, the probability of being disease free was100%.

Example System Implementation

According to one embodiment of any of the systems, the system executessoftware to determine a donor fraction (%) where the donor genotype isunknown. In one example, the execution includes any one or more or anycombination of the following operations:

1. A Monte Carlo simulation is executed across donor genotypes todetermine the possible donor fraction (in other embodiments, othermodels or approximations may be used);

2. Two phase approach, wherein an initial short simulation of samples(e.g., of a threshold number of samples (e.g., 1000, 2000, 3000, 4000,5000, 5999, among other options) is used to inform a secondarysimulation of a larger number of samples (e.g., 10000, 15000, 20000,25000, 29999, etc.)—in the simulation a median donor fraction, rCV and adQC triplet can be calculated;

3. In the initial simulation the evident donor genotypes can bedetermined by performing a generalized linear modeling of targetselections' influence on rCV and dQC separately. Further analysis of theentropy and frequency of target selections among high-background samplesare added to a donor genotype likelihood offset term;

4. In the initial simulation, donor genotypes can be chosen uniformly(e.g., set as 22.7% RR, 45.5% RV, 22.7% VV, 10% NA) (e.g. heterozygous(RV), homozygous variant (VV) and homozygous reference (RR);

5. The secondary simulation chooses donor genotypes as 25% RR, 50% RV,and 75% VV, with a uniform random variable offset by the above evidencevector, less two targets for unbiasing;

6. A three-dimensional point cloud is created and a portion censored.Simulations with extreme values of median donor fraction and rCV asdefined by an exponential function 0.001/3+(exp(3*x)−1)/2750 which aremarked for censoring. In some embodiments, if more than 95% of thesimulations are to be censored, the algorithm can be configured torecover those above their midpoint of median donor fractions;

7. Of the remaining simulations, the lower background noise simulationsare identified as those below the first quartile of dQC. According toany one of the system or method embodiments, simulations above the lowerquartile of dQC are discarded;

8. Of the remaining simulations, the internally consistent simulationsare identified as those below the first third of rCV. According to anyone of the system or method embodiments, simulations above the lowerthird of rCV can be discarded. In other examples, different cut offs canbe implemented for rCV;

9. In any one of the system or method embodiments, a calibration can beincluded in the donor analysis execution, for example, the donorfractions can be scaled by a linear formula (e.g.,y<−(1.166002)x+0.0001230337); and

10. In any one of the system or method embodiments, the algorithm isconfigured to capture the 48th percentile of median donor fractions arereturn that information.

FIG. 5 is a block diagram of platform 500 including system elements andfunctions for analyzing a sample, according to one embodiment. Invarious embodiments, platform 500 can receive or generate the data to beanalyzed. For example, the system can capture data from externaldatabase (e.g., 550, 552) and analyze the captured data. In otherexamples, users (e.g., 554, 556) can manage or trigger the communicationof the data to the platform 500. In further examples, users (658, 560)can operate assay devices and/or amplification devices (e.g., 582, 584and the results provided directly to the platform 500.

According to various embodiments of any one of the systems or methods,the analysis performed can be described by three phases: bGTpreprocessing, gGT preprocessing, and quantitative genotype processingand the results output at 592 and/or stored (e.g., in database 590).

In some embodiments, run and sample information (e.g., basic genotypingrun information 502 and/or quantitative genotyping run information 504)is captured through operation of a graphical user interface. In someexamples, basic genotyping preprocessing operates with information whichcan include specification of operator name, sample identifier, andsample location; quantitative genotype preprocessing can operate withinformation which can include run name, operator name, sampleidentifier, and sample location; and outcome call processing operateswith information which can include bGT preprocessing data files, filedesignation (recipient or donor), qGT preprocessing data files, run nameand sample name. The configuration database 594 can include informationspecifying data format, control information, and data on otherfunctions, including administrative functions.

Shown in FIG. 5, data from lightcycler 480 (e.g., 582 and 584) isprocessed as part of sample analysis. In one example, the platform 500captures data from ROCHE Lighcycler 480 via XML files or other suitabledata format. The data can be communicated with user management (e.g.,triggered by users 558 or 560).

Shown in FIG. 5 at 518 are three workflows which operate on runinformation obtained (e.g., 506 and 508), liquid handling information(e.g., 510 and 512), and RT-PCR data (e.g., 514 and 516 (which caninclude, for example, real time PCR data). The three workflows include:bGT preprocessing 522 which reads data obtained on a genomic DNA sample(for example, in conjunction with a plate layout configurationinformation) to generate a data file (e.g., binary data file) consistingof basic genotyping results and quality control documents—these filescan be archived on separate data repositories or systems; qGTpreprocessing 518 which reads data on a cell-free DNA sample (forexample, in conjunction with a plate layout configuration) to generate adata file (e.g., binary data file) consisting of quantitative genotypingresults and quality control documents—these files can be archived onseparate data repositories or systems; and quantitative genotypeprocessing 520 where a pair of basic genotyping and quantitativegenotyping data files (e.g., from 518 and 520) are analyzed to generatethe outcome measures and overall quality control documents—these filescan be archived on separate data source or systems including, forexample, database 590. In various embodiments, the results 592 can bedisplayed by the platform or communicated to other systems for display.

What is claimed is:
 1. A method, comprising: analyzing amounts ofalleles at multiple respective targets in a sample, and identifyquantifiable and/or informative targets, within the sample; performingsimulations with possible genotypes for a non-subject; and determiningamounts of alleles of each target attributed to the non-subject and,optionally, the subject, based on probable non-subject genotype(s)determined from the simulation, and, optionally, determining a percentor ratio of non-subject to subject amounts in the sample.
 2. The methodof claim 1, wherein the method further comprises determining the subjectgenotype.
 3. The method of any one of claim 1 or 2, wherein the methodfurther comprises performing amplifications to determine the amounts ofalleles.
 4. The method of 3, wherein the amplifications are performedfor at least 30, 40, 50, 60, 70, 80, 90, or more targets.
 5. The methodof any one of the preceding claims, further comprising calculatingquality measures on determined percents or ratios in the sample.
 6. Themethod of any one of the preceding claims, wherein the method comprisessimulating a likely non-subject genotype space.
 7. The method of any oneof the preceding claims, wherein simulations (e.g., Monte Carlo) areperformed to determine a range of probable genotypes for thenon-subject.
 8. The method of any one of the preceding claims, whereinthe method further comprises adjusting measured contributions forrespective targets based on respective probable genotypes (e.g.,doubling measured contribution value responsive to determining thenon-subject probable genotype is heterozygous).
 9. The method of any oneof the preceding claims, wherein the method further comprisescalculating an average, such as median, percent or ratio.
 10. The methodof any one of the preceding claims, wherein the method further comprisesdetermining each standard curve and/or sample amplification value meetsa confidence threshold.
 11. The method of any one of the precedingclaims, wherein the method further comprises determining confidencevalues based on analysis of at least one of a historic amplificationshape, specificity of the allele-specific PCR assay (e.g., with respectto a second allele), signal to noise ratio for a sample, slope andr-square value for standard curve sets, non-amplification valuesobtained on inserted controls, or contamination values obtained on thesample from negative controls.
 12. The method of any one of thepreceding claims, wherein the method further comprises fitting dataobtained from the sample to a historic amplification shape.
 13. Themethod of any one of the preceding claims, wherein the method furthercomprises determining the slope and r-square value for the standardcurve sets does not exceed a threshold value.
 14. The method of any oneof the preceding claims, wherein the method further comprisesestablishing a label for the non-subject or subject at each targetidentified as quantifiable and/or informative in the sample.
 15. Themethod of any one of the preceding claims, wherein the method furthercomprises determining informative targets within the sample responsiveto classifying a respective target according to genotype.
 16. The methodof any one of the preceding claims, wherein the method further comprisesclassifying the respective target as informative responsive todetermining the subject and non-subject have different genotypes (e.g.,the subject is homozygous for one allele and the non-subject is nothomozygous or homozygous for the other allele).
 17. The method of anyone of the preceding claims, wherein the method further comprisesadjusting measured contributions for a respective target responsive todetermining the non-subject is heterozygous (e.g., doubling measuredcontribution value responsive to determining the non-subject isheterozygous).
 18. The method of any one of the preceding claims,wherein the method further comprises calculating a median of informative(e.g., identified by the genotyping component) andquality-control-passed (e.g., identified by the quality controlcomponent) allele ratios and stores the median values as the ratio orpercentage.
 19. The method of any one of the preceding claims, whereinthe method further comprises calculating a regularized robustcoefficient of variation (“rCV”) based on a distribution of theinformative and quantifiable targets and associated percents or ratios.20. The method of any one of the preceding claims, wherein the methodfurther comprises calculating a robust standard deviation (“rSD”) basedon a median absolute divergence from a median minor species proportion.21. The method of any one of the preceding claims, wherein the methodfurther comprises converting the rSD into rCV by division with, forexample, the non-subject cf-DNA percentage.
 22. The method of any one ofthe preceding claims, wherein the method further comprises adjusting rSDto avoid division by zero (e.g., by adding a quarter of one percent tothe divisor).
 23. The method of any one of the preceding claims, whereinthe method further comprises identifying a sample suitable forquantification based on a threshold rCV value determined on adistribution of the informative and quantifiable targets and associatedpercents or ratios.
 24. The method of any one of the preceding claims,wherein the method further comprises evaluating an average minor alleleproportion of subject homozygous and non-informative targets against acontamination threshold.
 25. The method of any one of the precedingclaims, wherein the method further comprises calculating a discordancequality check (“dQC”) value based on the average minor allele proportionof the subject homozygous and the non-informative targets and evaluatethe dQC value against the threshold.
 26. The method of any one of thepreceding claims, wherein the method further comprises identifyingsamples suitable for quantification based on identifying a dQC valuebelow 0.5%
 27. The method of any one of the preceding claims, whereinthe non-subject is a donor.
 28. The method of any one of the precedingclaims, wherein the sample is from a transplant subject.
 29. The methodof claim 28, wherein the transplant subject is a heart transplantsubject.
 30. The method of claim 28 or 29, wherein the sample is from apediatric subject.
 31. The method of any one of the preceding claims,wherein the method further comprises selecting an aggregate and/or the95% confidence interval of the probable simulations.
 32. The method ofany one of the preceding claims, wherein the method further comprisesselecting simulations with below median dQC and rCV and/or determiningthe 95% confidence interval.
 33. A system for analyzing a sample from asubject, the system comprising: at least one processor operativelyconnected to a memory; a first component (e.g., a quality controlcomponent), executed by the at least one processor, configured toanalyze (e.g., quantitative genotyping (“qGT”)) amounts of alleles atmultiple respective targets in a sample, and identify quantifiableand/or informative targets, within the sample; a second component (e.g.,a modelling component) configured to simulate possible genotypeinformation for a non-subject; and a third component (e.g., a genotypingcomponent), executed by the at least one processor, configured todetermine amounts of alleles of each target attributed to thenon-subject and, optionally the subject, based on probable non-subjectgenotype(s) determined from the simulation, and, optionally, determininga percent or ratio of non-subject to subject amounts in the sample. 34.The system of claim 33, further comprising a fourth component (e.g., ananalytic component), executed by the at least one processor, configuredto calculate quality measures on determined percents or ratios in thesample.
 35. The system of any one of claim 33 or 34, wherein the thirdcomponent is configured to simulate a likely non-subject genotype space.36. The system of any one of claims 33-35, wherein the third componentis configured to execute a simulation (e.g., Monte Carlo) to determine arange of probable genotypes for the non-subject.
 37. The system of anyone of claims 33-36, wherein the third component is configured to adjustmeasured contributions for respective targets based on respectiveprobable genotypes (e.g., doubling measured contribution valueresponsive to determining the non-subject probable genotype isheterozygous).
 38. The system of any one of claims 33-37, wherein the atleast one processor is configured to calculate an average, such asmedian, percent or ratio.
 39. The system of any one of claims 33-38,wherein the first component is configured to determine each standardcurve and/or sample amplification value meets a confidence threshold.40. The system of any one of claims 33-39, wherein the first componentis configured to determine confidence values based on analysis of atleast one of a historic amplification shape, specificity of theallele-specific PCR assay (e.g., with respect to a second allele),signal to noise ratio for a sample, slope and r-square value forstandard curve sets, non-amplification values obtained on insertedcontrols, or contamination values obtained on the sample from negativecontrols.
 41. The system of claim 40, wherein the first component isconfigured to fit data obtained from the sample to a historicamplification shape.
 42. The system of claim 40, wherein the firstcomponent is configured to determine the slope and r-square value forthe standard curve sets does not exceed a threshold value.
 43. Thesystem of any one of claims 33-42, wherein the first or third componentis configured to establish a label for the non-subject or subject ateach target identified as quantifiable and/or informative in the sample.44. The system of claim 43, wherein the first or third component isconfigured to determine informative targets within the sample responsiveto classifying a respective target according to genotype.
 45. The systemof claim 43 or 44, wherein the third component is configured to classifythe respective target as informative responsive to determining thesubject and non-subject have different genotypes (e.g., the subject ishomozygous for one allele and the non-subject is not homozygous orhomozygous for the other allele).
 46. The system of any one of claims33-45, wherein the third component is configured to adjust measuredcontributions for a respective target responsive to determining thenon-subject is heterozygous (e.g., doubling measured contribution valueresponsive to determining the non-subject is heterozygous).
 47. Thesystem of any one of claims 33-46, wherein the third componentcalculates a median of informative (e.g., identified by the genotypingcomponent) and quality-control-passed (e.g., identified by the qualitycontrol component) allele ratios and stores the median values as theratio or percentage.
 48. The system of any one of claims 33-47, whereinany one of the components (e.g., the analytic component) is configuredto calculate a regularized robust coefficient of variation (“rCV”) basedon a distribution of the informative and quantifiable targets andassociated percents or ratios.
 49. The system of any one of claims33-48, wherein any one of the components (e.g., the analytic component)is configured to calculate a robust standard deviation (“rSD”) based ona median absolute divergence from a median minor species proportion. 50.The system of claim 49, wherein any one of the components (e.g., theanalytic component) is configured to convert the rSD into rCV bydivision with, for example, the non-subject cf-DNA percentage or ratio.51. The system of claim 49 or 50, wherein the component is configured toadjust rSD to avoid division by zero (e.g. by adding a quarter of onepercent).
 52. The system of any one of claims 33-51, wherein the systemis configured to identify a sample suitable for quantification based ona threshold rCV value determined on a distribution of the informativeand quantifiable targets and associated percents or ratios.
 53. Thesystem of any one of claims 33-52, wherein the system is configured toevaluate an average minor allele proportion of subject homozygous andnon-informative targets against a contamination threshold.
 54. Thesystem of claim 53, wherein the system is configured to calculate adiscordance quality check (“dQC”) value based on the average minorallele proportion of the subject homozygous and the non-informativetargets and evaluate the dQC value against the threshold.
 55. The systemof claim 53 or 54, wherein the system is configured to identify samplessuitable for quantification based on identifying a dQC value below 0.5%.56. The system of any one of claims 33-55, wherein the non-subject is adonor.
 57. The system of any one of claims 33-55, wherein the sample isfrom a transplant subject.
 58. The system of claim 57, wherein thetransplant subject is a heart transplant subject.
 59. The system ofclaim 57 or 58, wherein the sample is from a pediatric subject.
 60. Thesystem of any one of claims 33-59, wherein the system is furtherconfigured to select an aggregate and/or the 95% confidence interval ofthe probable simulations.
 61. The system of any one of claims 33-60,wherein the system is further configured to select simulations withbelow median dQC and rCV and/or determining the 95% confidence interval.62. A report comprising any one or more values that result from any oneof the preceding methods or systems.
 63. A method of treating a subject,comprising: evaluating a subject based on any one or more values thatresult from any one of the preceding methods or systems, and treating,recommending a treatment, changing a treatment, further monitoring orrecommending further monitoring of the subject.
 64. Any one of themethods as provided herein.
 65. Any one of the systems as providedherein.